Preparation: Remove documents, install packages, load data sets

# Remove all objects from R's memory
rm(list=ls())  
# We will use the following code to install all packages
packages <- c("tidyverse", # to load tidyverse
              "dplyr",# to load dplyr
              "ggplot2", # for plotting
              "haven", # for reading in data
              "plotly" # for interactive graphs
              )

# Install uninstalled packages
lapply(packages[!(packages %in% installed.packages())], install.packages)

# Load all packages to library
lapply(packages, library, character.only = TRUE)

Announcements

General resources

Data visualization is an entire science itself. It ranges from Tufte’s ink-information ratio to the best ways of delivering information.

ggplot2 basics

Like the general logic of the tidyverse, ggplot2 has a layered logic. That means we start off with a general object and add sequentially the parts to it that we want to have.

Here’s a short example:

Data + 
    Aesthetic mappings  +
    Layers (Geometric objects, stats) +
    Scales +
    Coordinate system +
    Faceting +
    Theme

It is based on the grammar of graphics – which is an idea that assumes that we can build every graph with a few components: data, geoms (visual marks that represent points), and a coordinate system. Translating that to the ggplot2 components, we need at least the first three layers (data, aesthetic mappings, and layers) to produce a plot.

Important: Instead of adding the %>% (pipe) operator here, you will need to use a +.

We will first explore the general logic of ggplot2.

To do so, we will (again) first load again the unpko dataset. We will now turn to the merged dataset from last time. I stored it for you with the name combined_data.RData.

load("combind_data.RData")

So, let’s translate the code above into ggplot2 and plot a simple scatterplot in ggplot.

ggplot(data = combined, # this is the data part
       aes(x = best, y = troop)) + # aesthetic mappings
  geom_point() # layers
## Warning: Removed 3181 rows containing missing values (geom_point).

So, we just did our very first plot!

ggplot2 allows us to nicely tweak and modify the plot. We could for example also color the points based on ccode.

ggplot(data = combined, # this is the data part
       aes(x = best, y = troop, # aesthetic mappings
           color = ccode)) + # add color
  geom_point() # layers
## Warning: Removed 3181 rows containing missing values (geom_point).

Apparently, the highest number of forces deployed are in Somalia – a country which apparently has a low number of battle-related deaths. As we know from history, the highest share of battle-related deaths can be found in Rwanda which also happens to be a case where the international community failed to provide (immediate) sufficient international support.

You can find more in the cheat sheet.

Replicating Figure 1 in Hultman et al. (2014)

Now we will turn to Figure 1 in the Hultman et al. (2014) paper and try to replicate it Let’s have a look at the data with a time component and let’s try to replicate Figure 1.

For this, we will load the unpko dataset.

unpko <- read_dta("Data/CMPS Mission Totals 1990-2011.dta")

ggplot allows us to use piped objects. This means we can use our already well-known filter() function to subset the dataset (in this case to the mission “UNMIL”) and plot the number of troops deployed.

unpko %>% 
  filter(mission == "UNMIL") %>% # Subset it to "UNMIL" only
  ggplot() + # call ggplot -- ATTENTION: we now use "+"
  aes(x = year, y = troop) + # select variables
  geom_point() # add the graph type

For the scatter plot we simply used geom_point(). Since Hultman et al. (2014) use a line graph in their paper, we are doing the same here and use geom_line().

unpko %>% 
  filter(mission == "UNMIL") %>% # Subset it to "UNMIL" only
  ggplot() + # call ggplot -- ATTENTION: we now use "+"
  aes(x = year, y = troop) + # select variables
  geom_line() # add the graph type

Have another look at Figure 1 – they also plot the mission “UNAVEM” in Angola. We will do the same as above and subset it to this mission.

unpko %>% 
  filter(mission == "UNAVEM") %>%
  ggplot() +
  aes(x = year, y = troop) + 
  geom_line()
## Warning: Removed 31 rows containing missing values (geom_path).

Now we need to add both to one graph. How do we do this? The approach I present you is one way of doing it. We will first generate two subsets of the unpko dataset by subsetting it to “UNMIL” and “UNAVEM”.

unpko_unmil <- unpko %>% 
  filter(mission == "UNMIL")

unpko_unavem <- unpko %>% 
  filter(mission == "UNAVEM")

We will then plot it by adding two geom_line() functions to the ggplot() function.

ggplot() +
  geom_line(data = unpko_unavem,
            aes(x = year, y = troop)) +
  geom_line(data = unpko_unmil,
            aes(x = year, y = troop))
## Warning: Removed 31 rows containing missing values (geom_path).

As you’ve seen, there are various ways how to call a ggplot(). I’ll show you three different ways at the example of the code used above.

# 1. version
ggplot(unpko_unavem,
            aes(x = year, y = troop)) +
            geom_line()
# 2. version
ggplot(unpko_unavem) +
            geom_line(aes(x = year, y = troop))
# 3. version
ggplot() +
  geom_line(data = unpko_unavem,
            aes(x = year, y = troop))

We used the third version because it allows us to specify a different dataset for each new geom_line() function.

While our graph looks similar to Hultman et al. (2014)’s graph, it still looks different. Why is this the case?

As you can see, Hultman et al. (2014) have a count variable in the x-axis whereas we have the years. We will now need to generate a count variable that counts the time of deployment (standardize it).

unpko_unavem$d <- 1

unpko_unavem$count <-
  with(unpko_unavem, ave(d == 1, cumsum(d == 0), FUN = cumsum))

unpko_unmil$d <- 1

unpko_unmil$count <-
  with(unpko_unmil, ave(d == 1, cumsum(d == 0), FUN = cumsum))

The count-variable counts up the years of deployment for each mission.

We basically now redo the steps we’ve done before but change year with count.

ggplot() +
  geom_line(data = unpko_unavem,
            aes(x = count, y = troop)) +
  geom_line(data = unpko_unmil,
            aes(x = count, y = troop)) 
## Warning: Removed 31 rows containing missing values (geom_path).

While the line for UNAVEM is slightly shifted, it already looks pretty much like Figure 1. The shift can be created by the count variable (the count variable just counts observations that are included in the unpko dataset and excludes all other data. if there are 0 troops deployed to UNAVEM it is probably not included in our dataset). For the purpose of showcasing ggplot2 this is totally fine with us.

We will now do some last finishes and add some axis titles and colors to make it “publication ready”.

ggplot() +
  geom_line(data = unpko_unavem,
            aes(x = count, y = troop), color = "gray") + # add color
  geom_line(data = unpko_unmil,
            aes(x = count, y = troop)) 
## Warning: Removed 31 rows containing missing values (geom_path).

We will now add the title:

ggplot() +
  geom_line(data = unpko_unavem,
            aes(x = count, y = troop), color = "gray") +
  geom_line(data = unpko_unmil,
            aes(x = count, y = troop)) +
  labs(title = "Longitudinal Variation in the Capacity of UN Mission Deployments 
                \nto Angola and Liberia") + # Add title
  xlab ("Time of Deployment") + # Add x axis description
  ylab("Number of UN Military Troops Deployed") # Add y axis description
## Warning: Removed 31 rows containing missing values (geom_path).

Since the titles appear to be a bit big, we will decrease the font sizes.

ggplot() +
  geom_line(data = unpko_unavem,
            aes(x = count, y = troop), color = "gray") +
  geom_line(data = unpko_unmil,
            aes(x = count, y = troop)) +
  labs(title = "Longitudinal Variation in the Capacity of UN Mission Deployments 
                \nto Angola and Liberia") + 
  xlab ("Time of Deployment") + 
  ylab("Number of UN Military Troops Deployed") + 
  theme(text = element_text(size=10)) # change the font sizes
## Warning: Removed 31 rows containing missing values (geom_path).

In a last step, we will add a minimalistic theme.

ggplot() +
  geom_line(data = unpko_unavem,
            aes(x = count, y = troop), color = "gray") +
  geom_line(data = unpko_unmil,
            aes(x = count, y = troop)) +
  labs(title = "Longitudinal Variation in the Capacity of UN Mission Deployments 
                \nto Angola and Liberia") + 
  xlab ("Time of Deployment") + 
  ylab("Number of UN Military Troops Deployed") + 
  theme(text = element_text(size=10)) +
  theme_minimal() # Add a minimalist theme
## Warning: Removed 31 rows containing missing values (geom_path).

Hands on exercise

Now I want you to become creative and start with your own visualization. Remember, I want you to submit a visualization of your expected mechanism. That means that you need to think about a creative way how to best display the relationship between your dependent variable and independent variable.

  1. Think about a visualization of your RQ. What is you DV? What is your IV? What would be a good way to visualize it? ca. 5 minutes

  2. Talk with your group about it. ca. 10 minutes

  3. Load your merged dataset. You might need to save it first (save(object_name, file = "file_name.RData")). Then load it (load("file_name.RData").) Pay attention to the path!

  4. Start with plotting your theoretical relationship. Remember the general structure of a ggplot.

Saving graphs

In a last step, we will learn how to save graphs. We will save the graphs in an object called plot1 first.

plot1 <- ggplot() +
  geom_line(data = unpko_unavem,
            aes(x = count, y = troop), color = "gray") +
  geom_line(data = unpko_unmil,
            aes(x = count, y = troop)) +
  labs(title = "Longitudinal Variation in the Capacity of UN Mission Deployments 
                \nto Angola and Liberia") + 
  xlab ("Time of Deployment") + 
  ylab("Number of UN Military Troops Deployed") + 
  theme(text = element_text(size=10)) +
  theme_minimal() 
  
ggsave(plot1, filename = "Plot1.png")
## Saving 7 x 5 in image
## Warning: Removed 31 rows containing missing values (geom_path).

Making interactive graphs

The package plotly allows us to easily generate interactive graphs. We simply wrap the function ggplotly() around our ggplot object; in this case it’s plot1.

ggplotly(plot1)

We can now hover the object and get precise information on every single observation point. This is a great tool for a general (web-)based visualization but also for data exploration.

Further readings

If you want to write your term paper in markdown, this R markdown workshop might be worth looking at.